---
title: "BubbleRAG — paper by Ye et al. (2026) — summary and ideas"
description: "Standard retrieval-augmented generation feeds all retrieved documents to an LLM in one pass. 80 references."
authors: ["Fuda Ye", "Jiachuan Wang", "Yongqi Zhang", "Lei Chen", "Shuangyin Li"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.2163/"
doi: "10.18653/v1/2026.findings-acl.2163"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ye-2026-bubblerag-interactive-cognitive-offloading-thought/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ye-2026-bubblerag-interactive-cognitive-offloading-thought/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/25/) / BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation

# BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation

**Authors:** Fuda Ye, Jiachuan Wang, [Yongqi Zhang](https://md.research.elamur.ai/free/en/authors/yongqi-zhang/), [Lei Chen](https://md.research.elamur.ai/free/en/authors/lei-chen/) and Shuangyin Li · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.2163/) · [PDF](https://aclanthology.org/2026.findings-acl.2163.pdf) · [DOI 10.18653/v1/2026.findings-acl.2163](https://doi.org/10.18653/v1/2026.findings-acl.2163) · [OpenAlex](https://openalex.org/W7166822359)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Standard retrieval-augmented generation feeds all retrieved documents to an LLM in one pass. The model must find relevant evidence, ignore noise and hold intermediate conclusions in memory without interacting with the text, which leads to problems like "lost-in-the-middle." BubbleRAG takes inspiration from how people use highlights and notes to reduce mental load. It adds a small trainable encoder, the "thought bubble," that sits outside a frozen backbone LLM. As the model reads each document step by step, the module turns its hidden state into bookmark tokens and inserts them back into the context. The model can then consult these as external memory during further reading and when answering. KV caching keeps inference cost manageable, and because the LLM is never fine-tuned, the approach suits low-resource settings. The authors report that BubbleRAG beats baseline RAG methods on effectiveness, robustness and generalizability. Attention analyses show it shifts focus toward relevant documents. This matters because it improves how a model uses evidence without retraining it.

## Key points
- The thought bubble takes the last token's hidden state from the LLM's final attention layer and turns it into bookmark tokens appended to each retrieved document. The backbone LLM stays frozen.
- Interactive reading is framed as an autoregressive, chunk-by-chunk process. KV caches are reused at inference to limit overhead.
- Attention heatmaps with Mistral 7B show different behavior from plain RAG:
  - Plain RAG over-attends to an irrelevant last-ranked document.
  - BubbleRAG shifts attention to relevant mid-list documents, especially in layers 16–32.
- Bookmark tokens keep high attention even in deep layers. Between layers 3 and 24 they form an arrow-shaped, sink-like "anchor" pattern.
- The paper lists several limitations:
  - The method needs access to hidden states, so it does not work with API-only models.
  - Generating bookmarks adds compute.
  - The bookmarks are not human-readable.
  - Testing is limited to textual QA.
- Code is available at github.com/yefd/BubbleRAG.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Hong Kong University of Science and Technology](https://md.research.elamur.ai/free/en/universities/hong-kong-university-of-science-and-technology/) — University, Hong Kong, China · 170 works · 26,341 citations
- [The Hong Kong University of Science and Technology (Guangzhou)](https://md.research.elamur.ai/free/en/universities/the-hong-kong-university-of-science-and-technology-guangzhou/) — University, Guangzhou, China · 67 works · 1,765 citations
- [University of Tsukuba](https://md.research.elamur.ai/free/en/universities/university-of-tsukuba/) — University, Tsukuba, Japan · 5 works · 945 citations
- [South China Normal University](https://md.research.elamur.ai/free/en/universities/south-china-normal-university/) — University, Guangzhou, China · 8 works · 24 citations

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,730 works

🧭 Topics (1): [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 552 works

## Cite

Cite as: Fuda Ye, Jiachuan Wang, Yongqi Zhang, Lei Chen and Shuangyin Li. 2026. BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ye2026bubblerag,
  title = {{BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation}},
  author = {Fuda Ye, Jiachuan Wang, Yongqi Zhang, Lei Chen and Shuangyin Li},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.2163},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.2163}
}
```

- [← previous BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents](https://md.research.elamur.ai/free/en/papers/ou-2026-browseconf-confidence-guided-test-time/)
- [next → Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data](https://md.research.elamur.ai/free/en/papers/zhang-2026-budget-aware-anytime-reasoning-llm/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/25/)

## References

80 references, 44 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. OpenAI Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mo Bavarian, Jeff Belgum, Irwan Bello, and 260 o… — [GPT-4 Technical Report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
2. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. [Self-rag: Learning to retrieve, generate, and critique through self-reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/). In International Conference on Learning Representations.
3. Ingeol Baek, Hwan Chang, ByeongJeong Kim, Jimin Lee, and Hwanhee Lee. 2025. Probing-RAG: Self-probing to guide language models in selective document retrieval. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 3287–3304, Albuquerque, New Mexico. Association for Computat… [find](https://scholar.google.com/scholar?q=Ingeol+Baek%2C+Hwan+Chang%2C+ByeongJeong+Kim%2C+Jimin+Lee%2C+and+Hwanhee+Lee%2E+2025%2E+Probing%2DRAG%3A+Self%2Dprobing+to+guide+language+models+in+selective+document+retrieval%2E+In+Findings+of+the+Association+for+Compu)
4. Jiaqi Bai, Hongcheng Guo, Jiaheng Liu, Jian Yang, Jian Yang, Xinnian Liang, Zhao Yan, and Zhoujun Li. 2023. Griprank: Bridging the gap between retrieval and generation via the generative knowledge improved passage ranking. Proceedings of the 32nd ACM International Conference on Information and Knowl… [find](https://scholar.google.com/scholar?q=Jiaqi+Bai%2C+Hongcheng+Guo%2C+Jiaheng+Liu%2C+Jian+Yang%2C+Jian+Yang%2C+Xinnian+Liang%2C+Zhao+Yan%2C+and+Zhoujun+Li%2E+2023%2E+Griprank%3A+Bridging+the+gap+between+retrieval+and+generation+via+the+generative+knowledge+imp)
5. Federico Barbero, Alvaro Arroyo, Xiangming Gu, Christos Perivolaropoulos, Petar Veličković, Razvan Pascanu, and Michael M. Bronstein. 2025. Why do LLMs attend to the first token? In Second Conference on Language Modeling. [find](https://scholar.google.com/scholar?q=Federico+Barbero%2C+Alvaro+Arroyo%2C+Xiangming+Gu%2C+Christos+Perivolaropoulos%2C+Petar+Velic%CC%8Ckovic%CC%81%2C+Razvan+Pascanu%2C+and+Michael+M%2E+Bronstein%2E+2025%2E+Why+do+LLMs+attend+to+the+first+token%3F+In+Second+Conferenc)
6. Tianchi Cai, Zhiwen Tan, Xierui Song, Tao Sun, Jiyan Jiang, Yunqi Xu, Yinger Zhang, and Jinjie Gu. 2024. Forag: Factuality-optimized retrieval augmented generation for web-enhanced long-form question answering. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,… [find](https://scholar.google.com/scholar?q=Tianchi+Cai%2C+Zhiwen+Tan%2C+Xierui+Song%2C+Tao+Sun%2C+Jiyan+Jiang%2C+Yunqi+Xu%2C+Yinger+Zhang%2C+and+Jinjie+Gu%2E+2024%2E+Forag%3A+Factuality%2Doptimized+retrieval+augmented+generation+for+web%2Denhanced+long%2Dform+question+)
7. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, and 1 others. 2024. Scaling instruction-finetuned language models. Journal of Machine Learning Research, 25(70):1–53. [find](https://scholar.google.com/scholar?q=Hyung+Won+Chung%2C+Le+Hou%2C+Shayne+Longpre%2C+Barret+Zoph%2C+Yi+Tay%2C+William+Fedus%2C+Yunxuan+Li%2C+Xuezhi+Wang%2C+Mostafa+Dehghani%2C+Siddhartha+Brahma%2C+and+1+others%2E+2024%2E+Scaling+instruction%2Dfinetuned+language+mo)
8. Andy Clark. 2010. Supersizing the mind: Embodiment, action, and cognitive extension. oxford university Press. [find](https://scholar.google.com/scholar?q=Andy+Clark%2E+2010%2E+Supersizing+the+mind%3A+Embodiment%2C+action%2C+and+cognitive+extension%2E+oxford+university+Press%2E)
9. Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. [Unsupervised cross-lingual representation learning at scale](https://md.research.elamur.ai/free/en/papers/conneau-2020-unsupervised-cross-lingual-representation-learning/). In Proceedings of the 58th Annual Meeting of the Association fo…
10. DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Jun-Mei Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiaoling Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, and 179 others. 2025. [Deepseek-r1: Incentivizing reasoning capability i…](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/)
11. DeepSeek-AI, Aixin Liu, Bei Feng, Bing Xue, Bing-Li Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dong-Li Ji, Erhang Li, Fangyun Lin, Fucong Dai, and 179 others. 2024. Deepseek-v3 technical report. ArXiv, abs/2412.19… [find](https://scholar.google.com/scholar?q=DeepSeek%2DAI%2C+Aixin+Liu%2C+Bei+Feng%2C+Bing+Xue%2C+Bing%2DLi+Wang%2C+Bochao+Wu%2C+Chengda+Lu%2C+Chenggang+Zhao%2C+Chengqi+Deng%2C+Chenyu+Zhang%2C+Chong+Ruan%2C+Damai+Dai%2C+Daya+Guo%2C+Dejian+Yang%2C+Deli+Chen%2C+Dong%2DLi+Ji%2C+Erhang)
12. Yihe Dong, Lorenzo Noci, Mikhail Khodak, and Mufan Li. 2025. Attention retrieves, mlp memorizes: Disentangling trainable components in the transformer. ArXiv, abs/2506.01115. — [arXiv:2506.01115](https://arxiv.org/abs/2506.01115) [find](https://scholar.google.com/scholar?q=Yihe+Dong%2C+Lorenzo+Noci%2C+Mikhail+Khodak%2C+and+Mufan+Li%2E+2025%2E+Attention+retrieves%2C+mlp+memorizes%3A+Disentangling+trainable+components+in+the+transformer%2E+ArXiv%2C+abs%2F2506%2E01115%2E+%E2%80%94+%5BarXiv%3A2506%2E01115%5D%28http)
13. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, Anirudh Goyal, Anthony S. Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, and 510 others.… — [The Llama 3 Herd of Models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
14. Jinyuan Fang, Zaiqiao Meng, and Craig MacDonald. 2024. TRACE the evidence: Constructing knowledge-grounded reasoning chains for retrieval-augmented generation. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8472–8494. [find](https://scholar.google.com/scholar?q=Jinyuan+Fang%2C+Zaiqiao+Meng%2C+and+Craig+MacDonald%2E+2024%2E+TRACE+the+evidence%3A+Constructing+knowledge%2Dgrounded+reasoning+chains+for+retrieval%2Daugmented+generation%2E+In+Findings+of+the+Association+for+Compu)
15. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Qianyu Guo, Meng Wang, and Haofen Wang. 2023. [Retrieval-augmented generation for large language models: A survey](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/). ArXiv, abs/2312.10997.
16. Zorik Gekhman, Gal Yona, Roee Aharoni, Matan Eyal, Amir Feder, Roi Reichart, and Jonathan Herzig. 2024. Does fine-tuning LLMs on new knowledge encourage hallucinations? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7765–7784, Miami, Florida, USA. As… [find](https://scholar.google.com/scholar?q=Zorik+Gekhman%2C+Gal+Yona%2C+Roee+Aharoni%2C+Matan+Eyal%2C+Amir+Feder%2C+Roi+Reichart%2C+and+Jonathan+Herzig%2E+2024%2E+Does+fine%2Dtuning+LLMs+on+new+knowledge+encourage+hallucinations%3F+In+Proceedings+of+the+2024+Conf)
17. Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Naik, Pengshan Cai, and Alfio Gliozzo. 2022. [Re2G: Retrieve, rerank, generate](https://md.research.elamur.ai/free/en/papers/glass-2022-re2g-retrieve-rerank-generate/). In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages…
18. Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. 2024. [Hipporag: Neurobiologically inspired long-term memory for large language models](https://md.research.elamur.ai/free/en/papers/gutierrez-2024-hipporag-neurobiologically-inspired-long-term/). In Proceedings of the 38th International Conference on Neural Information Processing Systems, NIPS ’24, Red Hook, NY, USA. Curran Associa…
19. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020. [Realm: Retrieval-augmented language model pre-training](https://md.research.elamur.ai/free/en/papers/guu-2020-realm-retrieval-augmented-language-model/). In International Conference on Machine Learning, pages 3929–3938.
20. Kyubeen Han, Junseo Jang, Hongjin Kim, Geunyeong Jeong, and Harksoo Kim. 2025. Exploring the impact of instruction-tuning on LLM’s susceptibility to misinformation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 26711– 26731,… [find](https://scholar.google.com/scholar?q=Kyubeen+Han%2C+Junseo+Jang%2C+Hongjin+Kim%2C+Geunyeong+Jeong%2C+and+Harksoo+Kim%2E+2025%2E+Exploring+the+impact+of+instruction%2Dtuning+on+LLM%E2%80%99s+susceptibility+to+misinformation%2E+In+Proceedings+of+the+63rd+Annual+M)
21. Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason E Weston, and Yuandong Tian. 2025. Training large language models to reason in a continuous latent space. In Workshop on Reasoning and Planning for Large Language Models. [find](https://scholar.google.com/scholar?q=Shibo+Hao%2C+Sainbayar+Sukhbaatar%2C+DiJia+Su%2C+Xian+Li%2C+Zhiting+Hu%2C+Jason+E+Weston%2C+and+Yuandong+Tian%2E+2025%2E+Training+large+language+models+to+reason+in+a+continuous+latent+space%2E+In+Workshop+on+Reasoning)
22. Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020. [Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps](https://md.research.elamur.ai/free/en/papers/ho-2020-constructing-multi-hop-qa-dataset/). In Proceedings of the 28th International Conference on Computational Linguistics, pages 6609– 6625.
23. Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. 2024. [Large language models cannot self-correct reasoning yet](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-cannot-self/). In International Conference on Learning Representations.
24. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021. [Unsupervised dense information retrieval with contrastive learning](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/). Transactions on Machine Learning Research, 2022.
25. Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2023. [Atlas: Few-shot learning with retrieval augmented language models](https://md.research.elamur.ai/free/en/papers/izacard-2022-atlas-few-shot-learning-retrieval/). Journal of Machine Learning Research, 24(251):1–43.
26. Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong Park. 2024. [Adaptive-RAG: Learning to adapt retrieval-augmented large language models through question complexity](https://md.research.elamur.ai/free/en/papers/jeong-2024-adaptive-rag-learning-adapt-retrieval/). In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Hu…
27. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc V. Le, and Slav Petrov. 2019. Na… — [Natural Questions: A Benchmark for Question Answering Research](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/)
28. Albert Qiaochu Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, L’elio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée La… [find](https://scholar.google.com/scholar?q=Albert+Qiaochu+Jiang%2C+Alexandre+Sablayrolles%2C+Arthur+Mensch%2C+Chris+Bamford%2C+Devendra+Singh+Chaplot%2C+Diego+de+Las+Casas%2C+Florian+Bressand%2C+Gianna+Lengyel%2C+Guillaume+Lample%2C+Lucile+Saulnier%2C+L%E2%80%99elio+Rena)
29. Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, and Nikolai Grigorev. 2022. [Internet-augmented language models through few-shot prompting for open-domain question answering](https://md.research.elamur.ai/free/en/papers/lazaridou-2022-internet-augmented-language-models-through/). ArXiv, abs/2203.05115.
30. Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. 2024. [LongLLMLingua: Accelerating and enhancing LLMs in long context scenarios via prompt compression](https://md.research.elamur.ai/free/en/papers/jiang-2023-longllmlingua-accelerating-enhancing-llms-long/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1:…
31. Bowen Jin, Jinsung Yoon, Jiawei Han, and Sercan O Arik. 2025. Long-context LLMs meet RAG: Overcoming challenges for long inputs in RAG. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Bowen+Jin%2C+Jinsung+Yoon%2C+Jiawei+Han%2C+and+Sercan+O+Arik%2E+2025%2E+Long%2Dcontext+LLMs+meet+RAG%3A+Overcoming+challenges+for+long+inputs+in+RAG%2E+In+International+Conference+on+Learning+Representations%2E)
32. Martin Johnson and Stuart Shaw. 2008. Annotating to comprehend: A marginalised activity? [find](https://scholar.google.com/scholar?q=Martin+Johnson+and+Stuart+Shaw%2E+2008%2E+Annotating+to+comprehend%3A+A+marginalised+activity%3F)
33. Tianjie Ju, Weiwei Sun, Wei Du, Xinwei Yuan, Zhaochun Ren, and Gongshen Liu. 2024. How large language models encode context knowledge? a layer-wise probing study. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 20… [find](https://scholar.google.com/scholar?q=Tianjie+Ju%2C+Weiwei+Sun%2C+Wei+Du%2C+Xinwei+Yuan%2C+Zhaochun+Ren%2C+and+Gongshen+Liu%2E+2024%2E+How+large+language+models+encode+context+knowledge%3F+a+layer%2Dwise+probing+study%2E+In+Proceedings+of+the+2024+Joint+Inte)
34. Zixuan Ke, Weize Kong, Cheng Li, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky. 2024. [Bridging the preference gap between retrievers and LLMs](https://md.research.elamur.ai/free/en/papers/ke-2024-bridging-preference-gap-between-retrievers/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10438–10451, Bangkok, Thailand…
35. Gangwoo Kim, Sungdong Kim, Byeongguk Jeon, Joonsuk Park, and Jaewoo Kang. 2023. [Tree of clarifications: Answering ambiguous questions with retrieval-augmented large language models](https://md.research.elamur.ai/free/en/papers/kim-2023-tree-clarifications-answering-ambiguous-questions/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 996–1009, Singapore.…
36. Diederik P. Kingma and Jimmy Ba. 2014. [A method for stochastic optimization](https://md.research.elamur.ai/free/en/papers/kingma-2014-adam-method-stochastic-optimization/). abs/1412.6980.
37. Adam: CoRR, David Kirsh. 2010. Thinking with external representations. AI Soc., 25(4):441–454. [find](https://scholar.google.com/scholar?q=Adam%3A+CoRR%2C+David+Kirsh%2E+2010%2E+Thinking+with+external+representations%2E+AI+Soc%2E%2C+25%284%29%3A441%E2%80%93454%2E)
38. Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. [The power of scale for parameter-efficient prompt tuning](https://md.research.elamur.ai/free/en/papers/lester-2021-power-scale-parameter-efficient-prompt/). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3045–3059, Online and Punta Cana, Dominican Republic. Association for Computational Linguist…
39. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, and 1 others. 2020. [Retrieval-augmented generation for knowledge-intensive nlp tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). In Proceedings of the 34th International Conference on Neur…
40. Bo Li, Tian Tian, Zhenghua Xu, Hao Cheng, Shikun Zhang, and Wei Ye. 2026a. Modeling uncertainty trends for timely retrieval in dynamic RAG. In Fortieth AAAI Conference on Artificial Intelligence, Thirty-Eighth Conference on Innovative Applications of Artificial Intelligence, Sixteenth Symposium on E… [find](https://scholar.google.com/scholar?q=Bo+Li%2C+Tian+Tian%2C+Zhenghua+Xu%2C+Hao+Cheng%2C+Shikun+Zhang%2C+and+Wei+Ye%2E+2026a%2E+Modeling+uncertainty+trends+for+timely+retrieval+in+dynamic+RAG%2E+In+Fortieth+AAAI+Conference+on+Artificial+Intelligence%2C+Thir)
41. Bo Li, Mingda Wang, Gexiang Fang, Shikun Zhang, and Wei Ye. 2026b. [Retrieval as generation: A unified framework with self-triggered information planning](https://md.research.elamur.ai/free/en/papers/li-2026-retrieval-as-generation-unified-framework/). ArXiv, abs/2604.11407.
42. Xiang Lisa Li and Percy Liang. 2021. Prefix-tuning: Optimizing continuous prompts for generation. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4582– 45… [find](https://scholar.google.com/scholar?q=Xiang+Lisa+Li+and+Percy+Liang%2E+2021%2E+Prefix%2Dtuning%3A+Optimizing+continuous+prompts+for+generation%2E+Proceedings+of+the+59th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+and+the+11th+I)
43. Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023. [Lost in the middle: How language models use long contexts](https://md.research.elamur.ai/free/en/papers/liu-2023-lost-middle-language-models-use/). Transactions of the Association for Computational Linguistics, 12:157–173.
44. Zhicheng Liu and John T. Stasko. 2010. Mental models, visual reasoning and interaction in information visualization: A top-down perspective. IEEE Transactions on Visualization and Computer Graphics, 16:999– 1008. [find](https://scholar.google.com/scholar?q=Zhicheng+Liu+and+John+T%2E+Stasko%2E+2010%2E+Mental+models%2C+visual+reasoning+and+interaction+in+information+visualization%3A+A+top%2Ddown+perspective%2E+IEEE+Transactions+on+Visualization+and+Computer+Graphics%2C+1)
45. Zhining Liu, Rana Ali Amjad, Ravinarayana Adkathimar, Tianxin Wei, and Hanghang Tong. 2025. [SelfElicit: Your language model secretly knows where is the relevant evidence](https://md.research.elamur.ai/free/en/papers/liu-2025-selfelicit-your-language-model-secretly/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9153– 9…
46. Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. When not to trust language models: Investigating effectiveness of parametric and non-parametric memories. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (V… [find](https://scholar.google.com/scholar?q=Alex+Mallen%2C+Akari+Asai%2C+Victor+Zhong%2C+Rajarshi+Das%2C+Daniel+Khashabi%2C+and+Hannaneh+Hajishirzi%2E+2023%2E+When+not+to+trust+language+models%3A+Investigating+effectiveness+of+parametric+and+non%2Dparametric+mem)
47. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022. Tra… — [Training language models to follow instructions with human feedback](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
48. Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, and Dongmei Zhang. 2024. [LLMLingua-2: Data distillation for efficient and faithful task-agnostic prompt compression](https://md.research.elamur.ai/free/en/papers/pan-2024-llmlingua-2-data-distillation-efficient/). In Findings of the Associat…
49. Muhammad Reza Qorib, Geonsik Moon, and Hwee Tou Ng. 2024. Are decoder-only language models better than encoder-only language models in understanding word meaning? In Findings of the Association for Computational Linguistics ACL 2024, pages 16339– 16347. [find](https://scholar.google.com/scholar?q=Muhammad+Reza+Qorib%2C+Geonsik+Moon%2C+and+Hwee+Tou+Ng%2E+2024%2E+Are+decoder%2Donly+language+models+better+than+encoder%2Donly+language+models+in+understanding+word+meaning%3F+In+Findings+of+the+Association+for+Co)
50. Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023. [In-context retrieval-augmented language models](https://md.research.elamur.ai/free/en/papers/ram-2023-context-retrieval-augmented-language-models/). Transactions of the Association for Computational Linguistics, 11:1316–1331.
51. Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, and et al. 2025. [Investigating the factual knowledge boundary of large language models with retrieval augmentation](https://md.research.elamur.ai/free/en/papers/ren-2025-investigating-factual-knowledge-boundary-large/). In Proceedings of the 31st International Conference on Computational Linguistics, pages 3697–3715. for open-domain question answerin…
52. Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Sohel Mondal, and Aman Chadha. 2024. [A systematic survey of prompt engineering in large language models: Techniques and applications](https://md.research.elamur.ai/free/en/papers/sahoo-2024-systematic-survey-prompt-engineering-large/). ArXiv, abs/2402.07927.
53. Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen. 2023. [Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9248–9274.
54. Tianyuan Shi, Liangzhi Li, Zijian Lin, Tao Yang, Xiaojun Quan, and Qifan Wang. 2023. Dual-feedback knowledge retrieval for task-oriented dialogue systems. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6566–6580, Singapore. Association for Computatio… [find](https://scholar.google.com/scholar?q=Tianyuan+Shi%2C+Liangzhi+Li%2C+Zijian+Lin%2C+Tao+Yang%2C+Xiaojun+Quan%2C+and+Qifan+Wang%2E+2023%2E+Dual%2Dfeedback+knowledge+retrieval+for+task%2Doriented+dialogue+systems%2E+In+Proceedings+of+the+2023+Conference+on+Empi)
55. Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Richard James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2024. [REPLUG: Retrieval-augmented black-box language models](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/). In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Hum…
56. John Sweller, Jeroen J. G. van Merriënboer, and Fred Paas. 2019. Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31:261 – 292. [find](https://scholar.google.com/scholar?q=John+Sweller%2C+Jeroen+J%2E+G%2E+van+Merri%C3%ABnboer%2C+and+Fred+Paas%2E+2019%2E+Cognitive+architecture+and+instructional+design%3A+20+years+later%2E+Educational+Psychology+Review%2C+31%3A261+%E2%80%93+292%2E)
57. Hugo Touvron, Louis Martin, Kevin R. Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Daniel M. Bikel, Lukas Blecher, Cristian Cantón Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, and… — [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/)
58. H. Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2021. Musique: Multi-hop questions via single-hop question composition. Transactions of the Association for Computational Linguistics, 10:539–554. [find](https://scholar.google.com/scholar?q=H%2E+Trivedi%2C+Niranjan+Balasubramanian%2C+Tushar+Khot%2C+and+Ashish+Sabharwal%2E+2021%2E+Musique%3A+Multi%2Dhop+questions+via+single%2Dhop+question+composition%2E+Transactions+of+the+Association+for+Computational+Lingu)
59. Evan F. Risko and Sam J. Gilbert. 2016. Cognitive offloading. Trends in Cognitive Sciences, 20(9):676– 688. [find](https://scholar.google.com/scholar?q=Evan+F%2E+Risko+and+Sam+J%2E+Gilbert%2E+2016%2E+Cognitive+offloading%2E+Trends+in+Cognitive+Sciences%2C+20%289%29%3A676%E2%80%93+688%2E)
60. Zhongwei Wan, Xinjian Wu, Yu Zhang, Yi Xin, Chaofan Tao, Zhihong Zhu, Xin Wang, Siqi Luo, Jing Xiong, Longyue Wang, and Mi Zhang. 2025. Dynamic discriminative operations for efficient generative inference of LLMs. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Zhongwei+Wan%2C+Xinjian+Wu%2C+Yu+Zhang%2C+Yi+Xin%2C+Chaofan+Tao%2C+Zhihong+Zhu%2C+Xin+Wang%2C+Siqi+Luo%2C+Jing+Xiong%2C+Longyue+Wang%2C+and+Mi+Zhang%2E+2025%2E+Dynamic+discriminative+operations+for+efficient+generative+infer)
61. Devendra Singh Sachan, Siva Reddy, William L. Hamilton, Chris Dyer, and Dani Yogatama. 2021. [End-to-end training of multi-document reader and retriever Lean Wang, Lei Li, Damai Dai, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, and Xu Sun](https://md.research.elamur.ai/free/en/papers/wang-2023-label-words-anchors-information-flow/). 2023. Label words are anchors: An information flow perspectiv…
62. Tevin Wang, Jingyuan He, and Chenyan Xiong. 2024a. RAGViz: Diagnose and visualize retrieval-augmented generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 320–327, Miami, Florida, USA. Association for Computational Lingui… [find](https://scholar.google.com/scholar?q=Tevin+Wang%2C+Jingyuan+He%2C+and+Chenyan+Xiong%2E+2024a%2E+RAGViz%3A+Diagnose+and+visualize+retrieval%2Daugmented+generation%2E+In+Proceedings+of+the+2024+Conference+on+Empirical+Methods+in+Natural+Language+Process)
63. Fuda Ye, Shuangyin Li, Yongqi Zhang, and Lei Chen. 2024. R 2 AG: Incorporating retrieval information into retrieval augmented generation. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 11584–11596. [find](https://scholar.google.com/scholar?q=Fuda+Ye%2C+Shuangyin+Li%2C+Yongqi+Zhang%2C+and+Lei+Chen%2E+2024%2E+R+2+AG%3A+Incorporating+retrieval+information+into+retrieval+augmented+generation%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+)
64. Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant. 2024. [Making retrieval-augmented language models robust to irrelevant context](https://md.research.elamur.ai/free/en/papers/yoran-2023-making-retrieval-augmented-language-models/). In International Conference on Learning Representations.
65. Zihao Wang, Anji Liu, Haowei Lin, Jiaqi Li, Xiaojian Ma, and Yitao Liang. 2024b. [Rat: Retrieval augmented thoughts elicit context-aware reasoning in long-horizon generation](https://md.research.elamur.ai/free/en/papers/wang-2024-rat-retrieval-augmented-thoughts-elicit/). ArXiv, abs/2403.05313.
66. Anqi Zhang, Yulin Chen, Jane Pan, Chen Zhao, Aurojit Panda, Jinyang Li, and He He. 2025. Reasoning models know when they’re right: Probing hidden states for self-verification. In Second Conference on Language Modeling. [find](https://scholar.google.com/scholar?q=Anqi+Zhang%2C+Yulin+Chen%2C+Jane+Pan%2C+Chen+Zhao%2C+Aurojit+Panda%2C+Jinyang+Li%2C+and+He+He%2E+2025%2E+Reasoning+models+know+when+they%E2%80%99re+right%3A+Probing+hidden+states+for+self%2Dverification%2E+In+Second+Conference+on+)
67. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H Chi, Quoc V Le, and Denny Zhou. 2022. [Chain of thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). In Proceedings of the 36th International Conference on Neural Information Processing Systems.
68. Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, and Bin Cui. 2024. [Retrieval-augmented generation for ai-generated content: A survey](https://md.research.elamur.ai/free/en/papers/zhao-2024-retrieval-augmented-generation-ai-generated/). ArXiv, abs/2402.19473.
69. Yuan Xia, Jingbo Zhou, Zhenhui Shi, Jun Chen, and Hai ting Huang. 2024. [Improving retrieval augmented language model with self-reasoning](https://md.research.elamur.ai/free/en/papers/xia-2024-improving-retrieval-augmented-language-model/). In AAAI Conference on Artificial Intelligence.
70. Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. 2024. Efficient streaming language models with attention sinks. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Guangxuan+Xiao%2C+Yuandong+Tian%2C+Beidi+Chen%2C+Song+Han%2C+and+Mike+Lewis%2E+2024%2E+Efficient+streaming+language+models+with+attention+sinks%2E+In+International+Conference+on+Learning+Representations%2E)
71. Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. 2023. [Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts](https://md.research.elamur.ai/free/en/papers/xie-2023-adaptive-chameleon-stubborn-sloth-revealing/). In International Conference on Learning Representations.
72. Shicheng Xu, Liang Pang, Jun Xu, Huawei Shen, and Xueqi Cheng. 2024. List-aware reranking-truncation joint model for search and retrieval-augmented generation. In Proceedings of the ACM on Web Conference 2024, pages 1330–1340. [find](https://scholar.google.com/scholar?q=Shicheng+Xu%2C+Liang+Pang%2C+Jun+Xu%2C+Huawei+Shen%2C+and+Xueqi+Cheng%2E+2024%2E+List%2Daware+reranking%2Dtruncation+joint+model+for+search+and+retrieval%2Daugmented+generation%2E+In+Proceedings+of+the+ACM+on+Web+Confere)
73. Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling. 2024. [Corrective retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/yan-2024-corrective-retrieval-augmented-generation/). ArXiv, abs/2401.15884.
74. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, and 41 others. 2025. [Qwen3 technical report](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/). ArXiv, abs/2505.09388.
75. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. [HotpotQA: A dataset for diverse, explainable multi-hop question answering](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages Wenz…
76. Yucheng Zhou, Xiubo Geng, Tao Shen, Chongyang Tao, Guodong Long, Jian-Guang Lou, and Jianbing Shen. 2023. Thread of thought unraveling chaotic contexts. ArXiv, abs/2311.08734. — [arXiv:2311.08734](https://arxiv.org/abs/2311.08734) [find](https://scholar.google.com/scholar?q=Yucheng+Zhou%2C+Xiubo+Geng%2C+Tao+Shen%2C+Chongyang+Tao%2C+Guodong+Long%2C+Jian%2DGuang+Lou%2C+and+Jianbing+Shen%2E+2023%2E+Thread+of+thought+unraveling+chaotic+contexts%2E+ArXiv%2C+abs%2F2311%2E08734%2E+%E2%80%94+%5BarXiv%3A2311%2E08734%5D%28htt)
77. Yujia Zhou, Zheng Liu, Jiajie Jin, Jian-Yun Nie, and Zhicheng Dou. 2024. Metacognitive retrieval-augmented large language models. Proceedings of the ACM Web Conference 2024. [find](https://scholar.google.com/scholar?q=Yujia+Zhou%2C+Zheng+Liu%2C+Jiajie+Jin%2C+Jian%2DYun+Nie%2C+and+Zhicheng+Dou%2E+2024%2E+Metacognitive+retrieval%2Daugmented+large+language+models%2E+Proceedings+of+the+ACM+Web+Conference+2024%2E)
78. Yilun Zhu, Joel Ruben Antony Moniz, Shruti Bhargava, Jiarui Lu, and et al. 2024. Can large language models understand context? In Findings of the Association for Computational Linguistics: EACL 2024, pages 2004–2018. [find](https://scholar.google.com/scholar?q=Yilun+Zhu%2C+Joel+Ruben+Antony+Moniz%2C+Shruti+Bhargava%2C+Jiarui+Lu%2C+and+et+al%2E+2024%2E+Can+large+language+models+understand+context%3F+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EACL+2024%2C+)
79. Yutao Zhu, Zhaoheng Huang, Zhicheng Dou, and Ji-Rong Wen. 2025a. One token can help! learning scalable and pluggable virtual tokens for retrieval-augmented large language models. In Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative… [find](https://scholar.google.com/scholar?q=Yutao+Zhu%2C+Zhaoheng+Huang%2C+Zhicheng+Dou%2C+and+Ji%2DRong+Wen%2E+2025a%2E+One+token+can+help%21+learning+scalable+and+pluggable+virtual+tokens+for+retrieval%2Daugmented+large+language+models%2E+In+Proceedings+of+the)
80. Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zheng Liu, Zhicheng Dou, and Ji-Rong Wen. 2025b. [Large language models for information retrieval: A survey](https://md.research.elamur.ai/free/en/papers/zhu-2023-large-language-models-information-retrieval/). ACM Trans. Inf. Syst., 44(1).

*Updated 2 November 2026*